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Description
Describe the bug
When I broadcast a range the compiler errors out with ERROR: Format of __nvvm__reflect function not recognized (the full error message is given at the bottom of this issue). This used to work and still does if I downgrade LLVM.jl to v4.16.0.
To reproduce
The Minimal Working Example (MWE) for this bug:
using CUDA
x = range(1.0, 2.0, step=0.1)
y = CuArray{eltype(x)}(undef, length(x))
@. y = x^2Manifest.toml
# This file is machine-generated - editing it directly is not advised
julia_version = "1.8.5"
manifest_format = "2.0"
project_hash = "0679e40a34cf12c10304de0ec173313bb3b4d468"
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deps = ["ChainRulesCore", "LinearAlgebra"]
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version = "1.3.1"
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deps = ["LinearAlgebra", "Requires"]
git-tree-sha1 = "cc37d689f599e8df4f464b2fa3870ff7db7492ef"
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git-tree-sha1 = "edff14c60784c8f7191a62a23b15a421185bc8a8"
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[[deps.Downloads]]
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[[deps.FileWatching]]
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[[deps.GPUArrays]]
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[[deps.IrrationalConstants]]
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[[deps.LLVM]]
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[[deps.LLVMExtra_jll]]
deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"]
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[[deps.LazyArtifacts]]
deps = ["Artifacts", "Pkg"]
uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
[[deps.LibCURL]]
deps = ["LibCURL_jll", "MozillaCACerts_jll"]
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[[deps.LibCURL_jll]]
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uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"
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[[deps.LibGit2]]
deps = ["Base64", "NetworkOptions", "Printf", "SHA"]
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[[deps.LibSSH2_jll]]
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[[deps.LinearAlgebra]]
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[[deps.MbedTLS_jll]]
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[[deps.MozillaCACerts_jll]]
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[[deps.OpenBLAS_jll]]
deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
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[[deps.OpenLibm_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
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[[deps.OpenSpecFun_jll]]
deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
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[[deps.Pkg]]
deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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[[deps.Preferences]]
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git-tree-sha1 = "47e5f437cc0e7ef2ce8406ce1e7e24d44915f88d"
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[[deps.Random]]
deps = ["SHA", "Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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[[deps.RandomNumbers]]
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uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
[[deps.TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
version = "1.0.0"
[[deps.Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
version = "1.10.1"
[[deps.Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[[deps.TimerOutputs]]
deps = ["ExprTools", "Printf"]
git-tree-sha1 = "f2fd3f288dfc6f507b0c3a2eb3bac009251e548b"
uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
version = "0.5.22"
[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.12+3"
[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.1.1+0"
[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.48.0+0"
[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"
Expected behavior
I would expect the @. y = x^2 statement to generate and call a broadcast kernel to fill y with x.^2. Which does happen when I downgrade the LLVM.jl version.
Version info
Details on Julia:
julia> versioninfo()
Julia Version 1.8.5
Commit 17cfb8e65ea (2023-01-08 06:45 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 32 × AMD Ryzen 9 7950X 16-Core Processor
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-13.0.1 (ORCJIT, znver3)
Threads: 1 on 32 virtual cores
Details on CUDA:
julia> CUDA.versioninfo()
CUDA runtime 11.8, artifact installation
CUDA driver 11.6
NVIDIA driver 510.108.3
Libraries:
- CUBLAS: 11.11.3
- CURAND: 10.3.0
- CUFFT: 10.9.0
- CUSOLVER: 11.4.1
- CUSPARSE: 11.7.5
- CUPTI: 18.0.0
- NVML: 11.0.0+510.108.3
Toolchain:
- Julia: 1.8.5
- LLVM: 13.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0, 7.1, 7.2
- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80, sm_86
1 device:
0: NVIDIA GeForce GTX TITAN X (sm_52, 10.507 GiB / 12.000 GiB available)
Additional context
Please find below the full error message.
ERROR: Format of __nvvm__reflect function not recognized
Stacktrace:
[1] error(s::String)
@ Base ./error.jl:35
[2] macro expansion
@ ~/.julia/packages/GPUCompiler/S3TWf/src/ptx.jl:439 [inlined]
[3] macro expansion
@ ~/.julia/packages/TimerOutputs/LHjFw/src/TimerOutput.jl:253 [inlined]
[4] nvvm_reflect!(fun::LLVM.Function)
@ GPUCompiler ~/.julia/packages/GPUCompiler/S3TWf/src/ptx.jl:413
[5] function_pass_callback(ptr::Ptr{Nothing}, data::Ptr{Nothing})
@ LLVM ~/.julia/packages/LLVM/X1AeZ/src/pass.jl:49
[6] LLVMRunPassManager
@ ~/.julia/packages/LLVM/X1AeZ/lib/13/libLLVM_h.jl:4898 [inlined]
[7] run!
@ ~/.julia/packages/LLVM/X1AeZ/src/passmanager.jl:39 [inlined]
[8] macro expansion
@ ~/.julia/packages/LLVM/X1AeZ/src/base.jl:102 [inlined]
[9] optimize_module!(job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget}, mod::LLVM.Module)
@ GPUCompiler ~/.julia/packages/GPUCompiler/S3TWf/src/ptx.jl:149
[10] optimize!(job::GPUCompiler.CompilerJob, mod::LLVM.Module)
@ GPUCompiler ~/.julia/packages/GPUCompiler/S3TWf/src/optim.jl:245
[11] macro expansion
@ ~/.julia/packages/GPUCompiler/S3TWf/src/driver.jl:342 [inlined]
[12] macro expansion
@ ~/.julia/packages/TimerOutputs/LHjFw/src/TimerOutput.jl:253 [inlined]
[13] macro expansion
@ ~/.julia/packages/GPUCompiler/S3TWf/src/driver.jl:341 [inlined]
[14] macro expansion
@ ~/.julia/packages/TimerOutputs/LHjFw/src/TimerOutput.jl:253 [inlined]
[15] macro expansion
@ ~/.julia/packages/GPUCompiler/S3TWf/src/driver.jl:331 [inlined]
[16] emit_llvm(job::GPUCompiler.CompilerJob, method_instance::Any; libraries::Bool, deferred_codegen::Bool, optimize::Bool, cleanup::Bool, only_entry::Bool, validate::Bool, ctx::LLVM.Context
)
@ GPUCompiler ~/.julia/packages/GPUCompiler/S3TWf/src/utils.jl:83
[17] cufunction_compile(job::GPUCompiler.CompilerJob, ctx::LLVM.Context)
@ CUDA ~/.julia/packages/CUDA/ZdCxS/src/compiler/execution.jl:360
[18] JuliaGPU/CUDA.jl#221
@ ~/.julia/packages/CUDA/ZdCxS/src/compiler/execution.jl:354 [inlined]
[19] JuliaContext(f::CUDA.var"#221#222"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#28", Tuple{CU
DA.CuKernelContext, CuDeviceVector{Float64, 1}, Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Tuple{Base.OneTo{Int64}}, typeof(Base.literal_pow), Tuple{CUDA.CuRefValue{typeof(^)}, Base.Bro
adcast.Extruded{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Tuple{Bool}, Tuple{Int64}}, CUDA.CuRefValue{Val{2}}}}, Int64}}}})
@ GPUCompiler ~/.julia/packages/GPUCompiler/S3TWf/src/driver.jl:76
[20] cufunction_compile(job::GPUCompiler.CompilerJob)
@ CUDA ~/.julia/packages/CUDA/ZdCxS/src/compiler/execution.jl:353
[21] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob, compiler::typeof(CUDA.cufunction_compile), linker::typeof(CUDA.cufunction_link))
@ GPUCompiler ~/.julia/packages/GPUCompiler/S3TWf/src/cache.jl:90
[22] cufunction(f::GPUArrays.var"#broadcast_kernel#28", tt::Type{Tuple{CUDA.CuKernelContext, CuDeviceVector{Float64, 1}, Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Tuple{Base.OneTo{Int
64}}, typeof(Base.literal_pow), Tuple{CUDA.CuRefValue{typeof(^)}, Base.Broadcast.Extruded{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Tuple{Bool}
, Tuple{Int64}}, CUDA.CuRefValue{Val{2}}}}, Int64}}; name::Nothing, always_inline::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ CUDA ~/.julia/packages/CUDA/ZdCxS/src/compiler/execution.jl:306
[23] cufunction @ ~/.julia/packages/CUDA/ZdCxS/src/compiler/execution.jl:299 [inlined] [24] macro expansion @ ~/.julia/packages/CUDA/ZdCxS/src/compiler/execution.jl:102 [inlined]
[25] #launch_heuristic#245 @ ~/.julia/packages/CUDA/ZdCxS/src/gpuarrays.jl:17 [inlined]
[26] _copyto! @ ~/.julia/packages/GPUArrays/XR4WO/src/host/broadcast.jl:65 [inlined]
[27] materialize! @ ~/.julia/packages/GPUArrays/XR4WO/src/host/broadcast.jl:41 [inlined]
[28] materialize!(dest::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}, bc::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Nothing, typeof(Base.literal_pow), Tuple{Base.RefValue{typeof(^)}, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Base.RefValue{Val{2}}}})
@ Base.Broadcast ./broadcast.jl:868 [29] top-level scope
@ REPL[6]:1 [30] top-level scope
@ ~/.julia/packages/CUDA/ZdCxS/src/initialization.jl:155
tgymnich and bluesycy
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